Cyber-Physical Data Fusion in Surrogate- Assisted Strength Pareto Evolutionary Algorithm for PHEV Energy Management Optimization
Ji Li, Quan Zhou, H. Leverne Williams, Hongming Xu, Changqing Du
Abstract
This article proposes a new form of algorithm environment for the multiobjective optimization of an energy management system in plug-in hybrid vehicles (PHEVs). The surrogate-assisted strength Pareto evolutionary algorithm (SSPEA) is developed to optimize the power-split control parameters guided by the data from the physical PHEV and its digital twins (DTs). By introducing a “confidence factor,” the SSPEA uses the fused data of physically measured and virtually simulated vehicle performances (energy consumption and remaining battery state of charge) to converge the optimization process. Gaussian noisy models are adopted to emulate the real vehicle system on the hardware-in-the-loop platform for experimental evaluation. The testing results suggest that the proposed SSPEA requires less R&D costs than the model-free method that only uses the physical information, and more than 44.6% energy can be saved during the R&D process. Driven by the SSPEA, the optimized energy management system surpasses other non-DT-assisted systems by saving more than 4.8% energy.